MyLab Statistics with Pearson eText Access Code (24 Months) for Statistics for Business

Robert Stine holds a Ph.D. from Princeton University. He has taught at the Wharton School since 1983, during which time he has regularly taught business  statistics. During his tenure, Bob has received a variety of teaching awards, including regularly winning the MBA Core Teaching Award, which is presented to faculty for outstanding teaching of the required curriculum at Wharton. He also received the David W. Hauck Award for Outstanding Teaching, awarded to the most highly rated faculty member teaching in the Wharton undergraduate program. Bob actively consults for industry. His clients include the pharmaceutical firms Merck and Pfizer, and he regularly works with the Federal Reserve Bank of Philadelphia on models for retail credit risk. This collaboration has produced three well-received conferences held at Wharton. His areas of research include computer software, time series analysis and forecasting, and general problems related to model identification and selection. Bob has published numerous articles in research journals, including the Journal of the American Statistical Association, Journal of the Royal Statistical Society, Biometrika, and The Annals of Statistics.   Dean Foster holds a Ph.D. from the University of Maryland. He has taught at the Wharton School since 1992 and previously taught at the University of Chicago. Dean teaches courses in introductory business statistics, probability and  Markov chains, statistical computing and advanced statistics for managers. Dean’s research areas are statistical inference for stochastic processes, game theory, machine learning, and variable selection. He is published in a wide variety of journals, including The Annals of Statistics, Operations Research, Games and Economic Behaviour, Journal of Theoretical Population Biology, and Econometrica.

I. Variation

Introduction
Data
Describing Categorical Data
Describing Numerical Data
Association Between Categorical Variables
Association Between Quantitative Variables

II. Probability

Probability
Conditional Probability
Random Variables
Association Between Random Variables
Probability Models for Counts
The Normal Probability Model

III. Inference

Samples and Surveys
Sampling Variation and Quality
Confidence Intervals
Statistical Tests
Comparison
Inference for Counts

IV. Regression Models

Linear Patterns
Curved Patterns
The Simple Regression Model
Regression Diagnostics
Multiple Regression
Building Regression Models
Categorical Explanatory Variables
Analysis of Variance
Time Series

Supplementary Material (Online-Only) Alternative Approaches to Inference Two-Way Analysis of Variance Regression with Big Data